We propose a novel approach to fully automatic lesion boundary detection in ultrasound breast images. The novelty of
the proposed work lies in the complete automation of the manual process of initial Region-of-Interest (ROI) labeling and
in the procedure adopted for the subsequent lesion boundary detection. Histogram equalization is initially used to preprocess
the images followed by hybrid filtering and multifractal analysis stages. Subsequently, a single valued
thresholding segmentation stage and a rule-based approach is used for the identification of the lesion ROI and the point
of interest that is used as the seed-point. Next, starting from this point an Isotropic Gaussian function is applied on the
inverted, original ultrasound image. The lesion area is then separated from the background by a thresholding
segmentation stage and the initial boundary is detected via edge detection. Finally to further improve and refine the
initial boundary, we make use of a state-of-the-art active contour method (i.e. gradient vector flow (GVF) snake model).
We provide results that include judgments from expert radiologists on 360 ultrasound images proving that the final
boundary detected by the proposed method is highly accurate. We compare the proposed method with two existing stateof-
the-art methods, namely the radial gradient index filtering (RGI) technique of Drukker et. al. and the local mean
technique proposed by Yap et. al., in proving the proposed method’s robustness and accuracy
